Accurately achieving target busulfan exposure in children and adolescents with very limited sampling and the bestdose software

Michael Neely, Michael Philippe, Teresa Rushing, Xiaowei Fu, Michael Van Guilder, David Bayard, Alan Schumitzky, Nathalie Bleyzac, Sylvain Goutelle

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Background: Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden. Methods: The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building. Results: Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment modelpredicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (#12 kg, 67% in the target range) and 1.0 mg/kg (.12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (22.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was 29.2% (216.7% to 21.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (20.3% to 4.2%, P = 0.08). Conclusions: BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

Original languageEnglish (US)
Pages (from-to)332-342
Number of pages11
JournalTherapeutic Drug Monitoring
Volume38
Issue number3
DOIs
StatePublished - Jan 1 2016

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Busulfan
Software
Bayes Theorem
Confidence Intervals
Weights and Measures
Population

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmacology (medical)

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Accurately achieving target busulfan exposure in children and adolescents with very limited sampling and the bestdose software. / Neely, Michael; Philippe, Michael; Rushing, Teresa; Fu, Xiaowei; Van Guilder, Michael; Bayard, David; Schumitzky, Alan; Bleyzac, Nathalie; Goutelle, Sylvain.

In: Therapeutic Drug Monitoring, Vol. 38, No. 3, 01.01.2016, p. 332-342.

Research output: Contribution to journalArticle

Neely, M, Philippe, M, Rushing, T, Fu, X, Van Guilder, M, Bayard, D, Schumitzky, A, Bleyzac, N & Goutelle, S 2016, 'Accurately achieving target busulfan exposure in children and adolescents with very limited sampling and the bestdose software', Therapeutic Drug Monitoring, vol. 38, no. 3, pp. 332-342. https://doi.org/10.1097/FTD.0000000000000276
Neely, Michael ; Philippe, Michael ; Rushing, Teresa ; Fu, Xiaowei ; Van Guilder, Michael ; Bayard, David ; Schumitzky, Alan ; Bleyzac, Nathalie ; Goutelle, Sylvain. / Accurately achieving target busulfan exposure in children and adolescents with very limited sampling and the bestdose software. In: Therapeutic Drug Monitoring. 2016 ; Vol. 38, No. 3. pp. 332-342.
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N2 - Background: Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden. Methods: The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building. Results: Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment modelpredicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (#12 kg, 67% in the target range) and 1.0 mg/kg (.12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (22.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was 29.2% (216.7% to 21.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (20.3% to 4.2%, P = 0.08). Conclusions: BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

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